Elizabeth Healey
Ph.D. Candidate working on Machine Learning for Healthcare
Hello! I am a final year PhD candidate in the Harvard–MIT Health Sciences and Technology program advised by Isaac Kohane and supported by the National Science Foundation GRFP. My research focus is in harnessing physiological signals and observational health data to enhance clinical decision-making and understand disease heterogeneity. My ongoing work concentrates on using continuous glucose monitoring to enable precision medicine for patients with type 2 diabetes.
Previously, I graduated magna cum laude from Harvard College with an S.B. degree in Electrical Engineering where I worked on biomedical control for the artificial pancreas.
I can be contacted at ehealey@mit.edu
- Machine learning for precision medicine
- Clinical decision support
- Wearable signal processing
- Diabetes technology
Massachusetts Institute of Technology
PhD in Medical Engineering and Medical Physics
Harvard University
BSc in Electrical Engineering, 2019
Research
Selected publications are below
Elizabeth Healey, Amelia Tan, Kristen Flint, Jessica Ruiz, Isaac Kohane. Leveraging Large Language Models to Analyze Continuous Glucose Monitoring Data: A Case Study. 2024.
Noah C Jones, Ming-Chieh Shih, Elizabeth Healey, Chen Wen Zhai, Sonali D Advani, Aaron Smith-McLallen, David Sontag, Sanjat Kanjilal. Reassessing the management of uncomplicated urinary tract infection: A retrospective analysis using machine learning causal inference. 2024.
Elizabeth Healey, Isaac Kohane. Model-Based Insulin Sensitivity and Beta-Cell Function Estimation from Daily Continuous Glucose Monitoring. Oral Presentation at the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2024. (in press).
Pathways Case Record: Profound Weight Loss In A Patient With Polymyositis And Small Bowel Inflammation. Advances in Motion. Massachusetts General Hospital. 2024.
Peniel Argaw*, Elizabeth Healey*, Isaac Kohane. Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals Proceedings of Machine Learning Research. 2022.
Ankush Chakrabarty, Elizabeth Healey, Dawei Shi, Stamatina Zavitsanou, Francis J Doyle and Eyal Dassau. Embedded Model Predictive Control for a Wearable Artificial Pancreas. IEEE Transactions Control Systems and Technololgy. 2019.
Teaching
I have been on the teaching team of multiple graduate and undergraduate courses during my training across topics in electrical engineering, machine learning, and translational methods in bioinformatics. I was previously a Teaching Development Fellow and earned a teaching certificate by completing the Graduate Teaching Development tracks at MIT.